What really is the scientific method in research?
Maybe your high school biology teacher made you recite the scientific method. If you talk to most practicing researchers, they would probably say this method is “more a set of guidelines than an actual rule”; kind of like the pirate’s code. Publications disseminate research using the key terms of the scientific method: background, hypothesis, experimental design or methods, results and conclusion. However, this approach may not reflect the complex reality of conducting research, nor optimize productivity. This article evaluates the scientific method by investigating different ways to approach research, specifically the strong inference model.
Some may argue that without the backbone of the scientific method, the research process is convoluted and complex. But is complexity something to be avoided or embraced? Consider the ecologist, Eric Berlow, as he simplifies complexity in his TED talk. He supports the claim that complexity is not complicated but, rather, simplifies our understanding by encompassing the whole network. Sound like an oxymoron? This abstraction is distilled to a concrete example with a more direct interpretation of an infographic on U.S. strategy in Afghanistan (this infographic looks as overwhelming as food webs over the past ten years). Is the scientific method stripping science of its innate, remarkable complexity, or distilling it into a digestible brew?
Such complexity can arise through logic trees, and imagine designing experiments using one! In 1964, John Platt presented the concept of strong inference, stating that the development of logic trees and alternative hypotheses are essential to rapid advancement of scientific inquiry. Platt exposes the weakness of the classical scientific method’s single-hypothesis. Actively pursuing an answer to one specific hypothesis may cause confirmation bias at each level of the scientific method, including background research on the question, modes of approaching the question experimentally, data collection and interpretation. Blind and double-blind experiments attempt to control for this, but if the methodology is biased, does the data quantified still lack bias? Platt suggests with anecdotal evidence that greater scientific progress is made through inductive reasoning (metaphorically, casting a fishing net into an ocean of unknowns) and conducting thoughtful experiments to eliminate alternative hypotheses and produce subsequent logical ones, instead of simply going to one fishing spot at a time through the deductive reasoning, as outlined by the classical scientific method.
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